A neural networks-based method for single-phase harmonic content identification

A neural method is presented in this paper to identify the harmonic components of an ac controller. The components are identified by analyzing the single-phase current waveform. The method effectiveness is verified by applying it to an active power filter (APF) model dedicated to the selective harmonic compensation. Simulation results using theoretical and experimental data are presented to validate the proposed approach. © 2008 IEEE.

Semiautomatic dental recognition using a graph-based segmentation algorithm and teeth shapes features

Dental recognition is very important for forensic human identification, mainly regarding the mass disasters, which have frequently happened due to tsunamis, airplanes crashes, etc. Algorithms for automatic, precise, and robust teeth segmentation from radiograph images are crucial for dental recognition. In this work we propose the use of a graph-based algorithm to extract the teeth contours from panoramic dental radiographs that are used as dental features. In order to assess our proposal, we have carried out experiments using a database of 1126 tooth images, obtained from 40 panoramic dental radiograph images from 20 individuals. The results of the graph-based algorithm was qualitatively assessed by a human expert who reported excellent scores. For dental recognition we propose the use of the teeth shapes as biometric features, by the means of BAS (Bean Angle Statistics) and Shape Context descriptors. The BAS descriptors showed, on the same database, a better performance (EER 14%) than the Shape Context (EER 20%). © 2012 IEEE.

HPLC-DAD based method for the quantification of flavonoids in the hydroethanolic extract of Tonina fluviatilis Aubl. (Eriocaulaceae) and their radical scavenging activity

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Quantitation of malondialdehyde in gingival crevicular fluid by a high-performance liquid chromatography-based method

Lipid peroxidation (LPO) has been associated with periodontal disease, and the evaluation of malondialdehyde (MDA) in the gingival crevicular fluid (GCF), an inflammatory exudate from the surrounding tissue of the periodontium, may be useful to clarify the role of LPO in the pathogenesis of periodontal disease. We describe the validation of a method to measure MDA in the GCF using high-performance liquid chromatography. MDA calibration curves were prepared with phosphate-buffered solution spiked with increasing known concentrations of MDA. Healthy and diseased GCF samples were collected from the same patient to avoid interindividual variability. MDA response was linear in the range measured, and excellent agreement was observed between added and detected concentrations of MDA. Samples' intra- and interday coefficients of variation were below 6.3% and 12.4%, respectively. The limit of quantitation (signal/noise = 5) was 0.03 mu M. When the validated method was applied to the GCF, excellent agreement was observed in the MDA quantitation from healthy and diseased sites, and diseased sites presented more MDA than healthy sites (P < 0.05). In this study, a validated method for MDA quantitation in GCF was established with satisfactory sensitivity, precision, and accuracy. (C) 2012 Elsevier Inc. All rights reserved.

A Phase Space Box-counting based Method for Arrhythmia Prediction from Electrocardiogram Time Series

Arrhythmia is one kind of cardiovascular diseases that give rise to the number of deaths and potentially yields immedicable danger. Arrhythmia is a life threatening condition originating from disorganized propagation of electrical signals in heart resulting in desynchronization among different chambers of the heart. Fundamentally, the synchronization process means that the phase relationship of electrical activities between the chambers remains coherent, maintaining a constant phase difference over time. If desynchronization occurs due to arrhythmia, the coherent phase relationship breaks down resulting in chaotic rhythm affecting the regular pumping mechanism of heart. This phenomenon was explored by using the phase space reconstruction technique which is a standard analysis technique of time series data generated from nonlinear dynamical system. In this project a novel index is presented for predicting the onset of ventricular arrhythmias. Analysis of continuously captured long-term ECG data recordings was conducted up to the onset of arrhythmia by the phase space reconstruction method, obtaining 2-dimensional images, analysed by the box counting method. The method was tested using the ECG data set of three different kinds including normal (NR), Ventricular Tachycardia (VT), Ventricular Fibrillation (VF), extracted from the Physionet ECG database. Statistical measures like mean (μ), standard deviation (σ) and coefficient of variation (σ/μ) for the box-counting in phase space diagrams are derived for a sliding window of 10 beats of ECG signal. From the results of these statistical analyses, a threshold was derived as an upper bound of Coefficient of Variation (CV) for box-counting of ECG phase portraits which is capable of reliably predicting the impeding arrhythmia long before its actual occurrence. As future work of research, it was planned to validate this prediction tool over a wider population of patients affected by different kind of arrhythmia, like atrial fibrillation, bundle and brunch block, and set different thresholds for them, in order to confirm its clinical applicability.

Graph-based analysis of brain resting-state fMRI data in nocturnal frontal lobe epileptic patients

Il lavoro che ho sviluppato presso l'unità di RM funzionale del Policlinico S.Orsola-Malpighi, DIBINEM, è incentrato sull'analisi dati di resting state - functional Magnetic Resonance Imaging (rs-fMRI) mediante l'utilizzo della graph theory, con lo scopo di valutare eventuali differenze in termini di connettività cerebrale funzionale tra un campione di pazienti affetti da Nocturnal Frontal Lobe Epilepsy (NFLE) ed uno di controlli sani. L'epilessia frontale notturna è una peculiare forma di epilessia caratterizzata da crisi che si verificano quasi esclusivamente durante il sonno notturno. Queste sono contraddistinte da comportamenti motori, prevalentemente distonici, spesso complessi, e talora a semiologia bizzarra. L'fMRI è una metodica di neuroimaging avanzata che permette di misurare indirettamente l'attività neuronale. Tutti i soggetti sono stati studiati in condizioni di resting-state, ossia di veglia rilassata. In particolare mi sono occupato di analizzare i dati fMRI con un approccio innovativo in campo clinico-neurologico, rappresentato dalla graph theory. I grafi sono definiti come strutture matematiche costituite da nodi e links, che trovano applicazione in molti campi di studio per la modellizzazione di strutture di diverso tipo. La costruzione di un grafo cerebrale per ogni partecipante allo studio ha rappresentato la parte centrale di questo lavoro. L'obiettivo è stato quello di definire le connessioni funzionali tra le diverse aree del cervello mediante l'utilizzo di un network. Il processo di modellizzazione ha permesso di valutare i grafi neurali mediante il calcolo di parametri topologici che ne caratterizzano struttura ed organizzazione. Le misure calcolate in questa analisi preliminare non hanno evidenziato differenze nelle proprietà globali tra i grafi dei pazienti e quelli dei controlli. Alterazioni locali sono state invece riscontrate nei pazienti, rispetto ai controlli, in aree della sostanza grigia profonda, del sistema limbico e delle regioni frontali, le quali rientrano tra quelle ipotizzate essere coinvolte nella fisiopatologia di questa peculiare forma di epilessia.

Graph-Based Multi-Surface Segmentation of OCT Data Using Trained Hard and Soft Constraints

Optical coherence tomography (OCT) is a well-established image modality in ophthalmology and used daily in the clinic. Automatic evaluation of such datasets requires an accurate segmentation of the retinal cell layers. However, due to the naturally low signal to noise ratio and the resulting bad image quality, this task remains challenging. We propose an automatic graph-based multi-surface segmentation algorithm that internally uses soft constraints to add prior information from a learned model. This improves the accuracy of the segmentation and increase the robustness to noise. Furthermore, we show that the graph size can be greatly reduced by applying a smart segmentation scheme. This allows the segmentation to be computed in seconds instead of minutes, without deteriorating the segmentation accuracy, making it ideal for a clinical setup. An extensive evaluation on 20 OCT datasets of healthy eyes was performed and showed a mean unsigned segmentation error of 3.05 ±0.54 μm over all datasets when compared to the average observer, which is lower than the inter-observer variability. Similar performance was measured for the task of drusen segmentation, demonstrating the usefulness of using soft constraints as a tool to deal with pathologies.

Development of a physically-based method for delineation of hydrologically homogeneous regions and flood quantile estimation in ungauged basins via the index flood method

Regional flood frequency techniques are commonly used to estimate flood quantiles when flood data is unavailable or the record length at an individual gauging station is insufficient for reliable analyses. These methods compensate for limited or unavailable data by pooling data from nearby gauged sites. This requires the delineation of hydrologically homogeneous regions in which the flood regime is sufficiently similar to allow the spatial transfer of information. It is generally accepted that hydrologic similarity results from similar physiographic characteristics, and thus these characteristics can be used to delineate regions and classify ungauged sites. However, as currently practiced, the delineation is highly subjective and dependent on the similarity measures and classification techniques employed. A standardized procedure for delineation of hydrologically homogeneous regions is presented herein. Key aspects are a new statistical metric to identify physically discordant sites, and the identification of an appropriate set of physically based measures of extreme hydrological similarity. A combination of multivariate statistical techniques applied to multiple flood statistics and basin characteristics for gauging stations in the Southeastern U.S. revealed that basin slope, elevation, and soil drainage largely determine the extreme hydrological behavior of a watershed. Use of these characteristics as similarity measures in the standardized approach for region delineation yields regions which are more homogeneous and more efficient for quantile estimation at ungauged sites than those delineated using alternative physically-based procedures typically employed in practice. The proposed methods and key physical characteristics are also shown to be efficient for region delineation and quantile development in alternative areas composed of watersheds with statistically different physical composition. In addition, the use of aggregated values of key watershed characteristics was found to be sufficient for the regionalization of flood data; the added time and computational effort required to derive spatially distributed watershed variables does not increase the accuracy of quantile estimators for ungauged sites. This dissertation also presents a methodology by which flood quantile estimates in Haiti can be derived using relationships developed for data rich regions of the U.S. As currently practiced, regional flood frequency techniques can only be applied within the predefined area used for model development. However, results presented herein demonstrate that the regional flood distribution can successfully be extrapolated to areas of similar physical composition located beyond the extent of that used for model development provided differences in precipitation are accounted for and the site in question can be appropriately classified within a delineated region.

ANALYSIS AND VISUALIZATION OF FLOW FIELDS USING INFORMATION-THEORETIC TECHNIQUES AND GRAPH-BASED REPRESENTATIONS

Three-dimensional flow visualization plays an essential role in many areas of science and engineering, such as aero- and hydro-dynamical systems which dominate various physical and natural phenomena. For popular methods such as the streamline visualization to be effective, they should capture the underlying flow features while facilitating user observation and understanding of the flow field in a clear manner. My research mainly focuses on the analysis and visualization of flow fields using various techniques, e.g. information-theoretic techniques and graph-based representations. Since the streamline visualization is a popular technique in flow field visualization, how to select good streamlines to capture flow patterns and how to pick good viewpoints to observe flow fields become critical. We treat streamline selection and viewpoint selection as symmetric problems and solve them simultaneously using the dual information channel [81]. To the best of my knowledge, this is the first attempt in flow visualization to combine these two selection problems in a unified approach. This work selects streamline in a view-independent manner and the selected streamlines will not change for all viewpoints. My another work [56] uses an information-theoretic approach to evaluate the importance of each streamline under various sample viewpoints and presents a solution for view-dependent streamline selection that guarantees coherent streamline update when the view changes gradually. When projecting 3D streamlines to 2D images for viewing, occlusion and clutter become inevitable. To address this challenge, we design FlowGraph [57, 58], a novel compound graph representation that organizes field line clusters and spatiotemporal regions hierarchically for occlusion-free and controllable visual exploration. We enable observation and exploration of the relationships among field line clusters, spatiotemporal regions and their interconnection in the transformed space. Most viewpoint selection methods only consider the external viewpoints outside of the flow field. This will not convey a clear observation when the flow field is clutter on the boundary side. Therefore, we propose a new way to explore flow fields by selecting several internal viewpoints around the flow features inside of the flow field and then generating a B-Spline curve path traversing these viewpoints to provide users with closeup views of the flow field for detailed observation of hidden or occluded internal flow features [54]. This work is also extended to deal with unsteady flow fields. Besides flow field visualization, some other topics relevant to visualization also attract my attention. In iGraph [31], we leverage a distributed system along with a tiled display wall to provide users with high-resolution visual analytics of big image and text collections in real time. Developing pedagogical visualization tools forms my other research focus. Since most cryptography algorithms use sophisticated mathematics, it is difficult for beginners to understand both what the algorithm does and how the algorithm does that. Therefore, we develop a set of visualization tools to provide users with an intuitive way to learn and understand these algorithms.

Minimal local anesthetic volume for peripheral nerve block: a new ultrasound-guided, nerve dimension-based method

BACKGROUND AND OBJECTIVES: Nerve blocks using local anesthetics are widely used. High volumes are usually injected, which may predispose patients to associated adverse events. Introduction of ultrasound guidance facilitates the reduction of volume, but the minimal effective volume is unknown. In this study, we estimated the 50% effective dose (ED50) and 95% effective dose (ED95) volume of 1% mepivacaine relative to the cross-sectional area of the nerve for an adequate sensory block. METHODS: To reduce the number of healthy volunteers, we used a volume reduction protocol using the up-and-down procedure according to the Dixon average method. The ulnar nerve was scanned at the proximal forearm, and the cross-sectional area was measured by ultrasound. In the first volunteer, a volume of 0.4 mL/mm of nerve cross-sectional area was injected under ultrasound guidance in close proximity to and around the nerve using a multiple injection technique. The volume in the next volunteer was reduced by 0.04 mL/mm in case of complete blockade and augmented by the same amount in case of incomplete sensory blockade within 20 mins. After 3 up-and-down cycles, ED50 and ED95 were estimated. Volunteers and physicians performing the block were blinded to the volume used. RESULTS: A total 17 of volunteers were investigated. The ED50 volume was 0.08 mL/mm (SD, 0.01 mL/mm), and the ED95 volume was 0.11 mL/mm (SD, 0.03 mL/mm). The mean cross-sectional area of the nerves was 6.2 mm (1.0 mm). CONCLUSIONS: Based on the ultrasound measured cross-sectional area and using ultrasound guidance, a mean volume of 0.7 mL represents the ED95 dose of 1% mepivacaine to block the ulnar nerve at the proximal forearm.